Improving community rating in the tele-lecturing context

A lot of content for tele-lecturing portals was produced in the last decade. But metadata to filter the content was not generated adequately. It therefore takes a long time for users to filter through all the videos available until they find the learning content they are looking for. That is why it is a new challenge to find solutions how to filter the large amount of data with a small base of metadata available. To engage the user community to generate metadata is one option. This paper compares two rating functionalities as one community feature to enhance search functionalities in tele-teaching portals. The efficiency of the two algorithms is tested against each other using a plug-in-architecture to switch between the two options. One of the algorithms simply calculates a mean rating of all ratings for one content item. The other one applies a rating over several layers of content items and therefore generates more precise results and automatically rates connected content items.